code: def make_dataset(dataset, metrics):
      X = []
      for line in open(f"{dataset}.txt"):
          v = [float(x) for x in line.strip().split()]
          X.append(np.array(v))
      dim = len(X[0])
      X_train, X_test = train_test_split(
          X, test_size=10000, random_state=1
      )
  
      for metric in metrics:
          write_output(
              np.array(X_train),
              np.array(X_test),
              f"{dataset}-{dim}-{metric}.hdf5",
              metric,
          )
  
file_path: n2/benchmarks/data/create_youtube_dataset.py
imports: import sys
  import h5py
  import numpy
  from sklearn.model_selection import train_test_split
  from metrics import metrics
label_desc: Use image node_id 2 for calling a global function with example usage: make_dataset(dataset, metrics) without return types